In this exercise, you will load a filtered gapminder
dataset - with a subset of data on global development from 1952 - 2007
in increments of 5 years - to capture the period between the Second
World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.
First, start with installing and activating the relevant packages
tidyverse, gganimate, and
gapminder if you do not have them already. Pay
attention to what warning messages you get when installing
gganimate, as your computer might need other packages than
gifski and av
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Warning: pakke 'gganimate' blev bygget under R version 4.4.3
## Warning: pakke 'gifski' blev bygget under R version 4.4.3
## Warning: pakke 'av' blev bygget under R version 4.4.3
## Warning: pakke 'gapminder' blev bygget under R version 4.4.3
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
options(scipen = 999)
ggplot(data=subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(colour=continent)) +
scale_x_log10() +
ggtitle("1975")
#chatgpt for eliminating the scientific notation function and for mistakes in my code
gapminder %>%
filter(year==1952) %>%
slice_max(gdpPercap, n=1)
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
…
We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(colour = continent)) +
scale_x_log10() +
ggtitle("2007")
#used chatgpt to find a mistakes in my code
gapminder %>%
filter(year==2007) %>%
slice_max(gdpPercap, n=5)
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Answer: why does it make sense to have a log10 scale
(scale_x_log10()) on the x axis? (hint: try to comment
it out and observe the result) The function is useful because the values
doesn’t change but the function makes it easier to read. spreads the
values out.
Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis? Kuwait
Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)
Answer: What are the five richest countries in the world in 2007? Norway, Kuwait, Singapore, United States and Ireland
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
options(scipen=999)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
options(scipen = 999)
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year, )
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
transition_states() and transition_time()
functions respectively)options(scipen=999)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) +
labs(title = "Global Development in {frame_time}",
x = "GDP per capita (log scale)",
y = "Life Expectancy",
color = "Continent") +
theme_minimal() +
transition_time(year) +
ease_aes('linear')
animate(anim, renderer = gifski_renderer())
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) +
scale_size_continuous(labels = scales::comma) +
labs(title = "Global Development in {frame_time}",
x = "GDP per capita (log scale)",
y = "Life Expectancy",
color = "Continent") +
theme_minimal() +
transition_time(year) +
ease_aes('linear')+
theme(
plot.title = element_text(size=18, face="bold"),
axis.title.x = element_text(size=16, face="bold"),
axis.title.y = element_text(size=16, face="bold"),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14))
animate(anim, renderer = gifski_renderer())
gapminder_unfiltered dataset or
download more historical data at https://www.gapminder.org/data/ ]birth_year <- 2002
gapminder %>%
filter(year %in% c(birth_year, 2007)) %>%
group_by(year) %>%
summarise(avg_lifeExp=mean(lifeExp, na.rm=TRUE), avg_gdpPercap=mean(gdpPercap, na.rm=TRUE))
## # A tibble: 2 × 3
## year avg_lifeExp avg_gdpPercap
## <int> <dbl> <dbl>
## 1 2002 65.7 9918.
## 2 2007 67.0 11680.
ggplot(gapminder %>% filter(year %in% c(birth_year, 2007)),
aes(x = factor(year), y = lifeExp, fill = continent)) +
geom_boxplot() +
labs(title = "Life Expectancy Over Time",
x = "Year",
y = "Life Expectancy",
fill = "Continent") +
theme_minimal()
The question was “Is the World a better place today than the year I
was born?”, and this can be answered with various factors in mind. I
chose to plot Life Expectancy, and from my illustration and coding I can
conclude that the world now compared to the year 2002 is somewhat a
better place seen from the perspective of life expectancy. It has not
increased a lot. It is also only a 5 year difference, which does have an
effect on the results of the development. The higher life expectancy can
be caused by a lot of factors, such as better healthcare, living
standards, economy etc. Just to look at a broader perspective, I would
like to point out that the average gdp also increased from 2002 to 2007.
This contributes to the world being a better place. Over time both of
these numbers have increased over time, not just from 2002 to 2007 but
also before that. As I wrote, 5 years is not a long time, and does not
give full insight to Global development, than if you had chosen for
example 1950. If we take into count how the life expectancy in Kuwait -
from the first questions - in 1952, had a life expectancy at about 55,
the increase looks a little more dramatic with a 12 years
difference.
But all in all, the world is a better place for a longer life and more
general wealth, but does that mean it is a better world in other
ways.
In this assignment, I have used chatgpt for correcting mistakes in my coding, and for help solving and understanding the question